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Flink is an amazing technology; the power to process real-time data on the stream is something that everyone running Kafka, Pulsar, Kinesis and other popular messaging platforms will want to use eventually. But with power comes responsibility, and in the case of Flink, complexity. Decodable's mission is to eliminate this complexity to make stream processing available to everyone. In this blog and video we'll show the relative experience of two Flink-based cloud services; Decodable and Kinesis Data Analytics.
Connect - Build - Manage
Decodable abstracts the complexity of deploying and operating Flink as well as operating the underlying infrastructure so you can focus on the things that matter:
- Connect to data sources and sinks
- Build streams and pipelines that transport and transform data
- Manage running pipelines to ensure the reliable flow of data
Connections, Streams and Pipelines
As with any useful, opinionated abstraction, Decodable provides a simple set of objects to represent the underlying structures while hiding as much detail and complexity as possible so developers can focus on what really matters. Decodable's objects are:
- Connectors to a range of messaging systems, data stores and general protocols such as REST/HTTP
- Streams providing a persistent message store and transport between connectors and pipelines
- Pipelines which enable processing of streaming records using SQL for common operations such as transformations, filtering, aggregation, triggering and more.
Comparing Decodable and Kinesis Data Analytics
Both Decodable and Kinesis Data Analytics are based on a hosted version of Flink. The developer experience however is radically different, thanks to the high level of abstraction and opinionation in Decodable.
In the following video we walk through how the same example of stream processing is configured and operated first in Decodable and then in Kinesis Data Analytics.
You can get started with Decodable for free - our developer account includes enough for you to build a useful pipeline and - unlike a trial - it never expires.
Learn more:
- Read the docs
- Check out our other blogs
- Subscribe to our YouTube Channel
Join the community Slack
Keeping Things Simple
Why hasn’t stream processing experienced hockey stick growth that streaming has? In this post we explore what's behind this lag and why it's about to rapidly change.
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Webinar: Real-Time Data Engineering With Decodable
Learn more about Decodable's real-time data engineering platform, the solutions you can build with it, and how to get started. After this demo-heavy session you'll be ready to build streaming data transformations that work with your existing data infrastructure - it's fast, simple, and easy with Decodable.
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